Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques

Abstract

Brain–machine interface (BMI) technology captures brain signals to enable control of prosthetic or communication devices with the goal of assisting patients who have limited or no ability to perform voluntary movements. Decoding of inherent information in brain signals to interpret the user’s intention is one of main approaches for developing BMI technology. Subdural electrocorticography (sECoG)-based decoding provides good accuracy, but surgical complications are one of the major concerns for this approach to be applied in BMIs. In contrast, epidural electrocorticography (eECoG) is less invasive, thus it is theoretically more suitable for long-term implementation, although it is unclear whether eECoG signals carry sufficient information for decoding natural movements. We successfully decoded continuous three-dimensional hand trajectories from eECoG signals in Japanese macaques. A steady quantity of information of continuous hand movements could be acquired from the decoding system for at least several months, and a decoding model could be used for ∼10 days without significant degradation in accuracy or recalibration. The correlation coefficients between observed and predicted trajectories were lower than those for sECoG-based decoding experiments we previously reported, owing to a greater degree of chewing artifacts in eECoG-based decoding than is found in sECoG-based decoding. As one of the safest invasive recording methods available, eECoG provides an acceptable level of performance. With the ease of replacement and upgrades, eECoG systems could become the first-choice interface for real-life BMI applications.

Extracting kinetic information from human motor cortical signals

“…As such, they could provide an even safer alternative to ECoG while still allowing accurate control of movement (Rouse et al, 2013;Shimoda et al, 2012;Slutzky et al, 2011). Together, these intermediate signal sources could provide clinicians with an important signal source for BMI applications.…”

Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate

“…This potentially mitigates some inflammatory burden on the brain. ECoG signals have been found to encode information about arm and hand movements (Leuthardt et al, 2004; Schalk et al, 2007; Crone et al, 1998; Miller et al, 2007; Pistohl et al, 2008; Wang et al, 2009; Kubánek et al, 2009; Ball et al, 2009; Miller et al, 2009; Chao et al, 2010; Acharya et al, 2010; Degenhart et al, 2011a; Shimoda et al, 2012; Chestek et al, 2013; Nakanishi et al, 2013), as well as auditory (Edwards et al, 2005; Trautner et al, 2006), visual (Lachaux et al, 2005), language (Crone et al, 2001; Mainy et al, 2007; Kellis et al, 2010; Wang et al; 2011, Pei et al, 2011), and attentional processes (Tallon-Baudry et al, 2005; Jung et al, 2008; Ray et al, 2008). Encouraged by these findings, researchers have begun to investigate ECoG as a potential source of control signals for BMI devices.…”

Frequency-dependent spatiotemporal profiles of visual responses recorded with subdural ECoG electrodes in awake monkeys: Differences between high- and low-frequency activity

“…Recently, ECoG has gained attention as a tool for electrophysiological research in animals because it offers several advantages, including large spatial coverage, fine spatiotemporal resolution, and stable recordings over weeks to months, which are impossible to attain simultaneously with other recording techniques (Bosman et al, 2012;Chao et al, 2010;Matsuo et al, 2011;Rubehn et al, 2009;Shimoda et al, 2012;Viventi et al, 2011). These advantages make ECoG a promising technique for neuroengineering applications, including brain machine interfaces (Graimann et al, 2004;Schalk et al, 2008).…”